How do Russian and pro-Russian strategic narratives circulate within an information space that is officially closed to them? And why do certain audiences embrace them? Drawing on fieldwork conducted...

💡 What if the missing piece in fostering autonomy for young autistic people is not just working with them, but empowering their families? 🧠✨ Self-determination—the capacity to act as the causal agent...

✨ Advancing diversity and innovation in academia: we are proud to launch the project INTouch - Intersectionality and Collaborative Online International Learning. Connecting cultures and minds. Co-fun...

📢 Welcome to the ChildTech Lab, Dr. Sergi Fàbregues Feijóo! 🚀 We are thrilled to announce that Sergi is joining our team as a Senior Researcher focused on innovative research methods. With a PhD in ...

How can researchers use large language models without compromising causal rigor? Join our online seminar on Causal Inference and Large Language Models, designed for PhD students, postdoctoral researchers, and academic staff working with textual, social media, administrative, or mixed-format data. Taught by Moses Boudourides (Northwestern University), this seminar explores how LLMs can be integrated into causal inference workflows to measure latent constructs, generate candidate instruments, support causal discovery, estimate treatment effects with high-dimensional text data, and audit bias in LLM-derived variables. Participants will engage with conceptual foundations and applied workflows using reproducible, fully annotated Python and R scripts, diagnostic tools, and publication-oriented strategies. No prior knowledge of causal inference, LLMs, programming, or coding is required; general familiarity with quantitative research is sufficient. The seminar is delivered online via Zoom, with recordings, slides, scripts, data, readings, and a monitored Q&A forum available for 30 days after the seminar. Participants receive an official Instats certificate of completion, with ECTS Equivalent points where applicable. #AppliedResearch #BayesianStatistics #CausalInference #ComputationalSocialScience #ComputationalStatistics #ComputerScience #DataScience #Economics #EnvironmentalSciences #Finance #Healthcare #MachineLearning #Management #MixedMethods #OrganizationStudies #RegressionModelingStrategies #ResearchMethods #SocialSciences #Sociology #Statisticalmodellingandregression #Statistics #SocialTheory #Python #R #Research #ResearchTraining #Instats

By Margaret R. Roller, Doug Currivan. Mixed methods research — the integration of quantitative and qualitative data — enriches the researcher’s methodological framework and ultimately their knowledge ...

How can researchers use large language models without compromising the rigor of causal inference? This online Instats seminar, taught by Moses Boudourides (Northwestern University), explores how LLMs can be integrated into defensible causal workflows for textual, social media, administrative, and mixed-format data. Designed for PhD students, postdoctoral researchers, and academic staff, the seminar covers how to formalise causal questions, use LLMs to measure latent constructs, address prediction error and bias, generate and evaluate candidate instruments, support causal discovery, and estimate treatment effects with high-dimensional text data. No prior knowledge of causal inference, LLMs, or programming is required; participants receive fully annotated Python and R scripts, lecture slides, diagnostic tools, a curated reading list, access to recordings and materials for 30 days, a monitored Q&A forum, and an official Instats certificate of completion. #AppliedResearch #BayesianStatistics #CausalInference #ComputationalSocialScience #ComputationalStatistics #ComputerScience #DataScience #Economics #EnvironmentalSciences #Finance #Healthcare #MachineLearning #Management #MixedMethods #OrganizationStudies #RegressionModelingStrategies #ResearchMethods #SocialSciences #Sociology #Statisticalmodellingandregression #Statistics #SocialTheory #Python #R #Research #ResearchTraining #Instats

Join us for Questionnaire Design Principles and Practices: Examples from International Large-Scale Assessments (ILSAs), a one-day online workshop with Surette van Staden from the Institute for Teacher Education, University of Innsbruck, designed for PhD students, postdoctoral researchers, faculty, and professional researchers who design, adapt, or use questionnaires in educational and mixed-methods research. Drawing on best practices from PIRLS and other international large-scale assessments, this practical seminar will guide participants through the full questionnaire development process, from conceptual frameworks and item writing to translation, cognitive interviewing, piloting, measurement quality, documentation, and links to analytic strategies such as scaling and handling measurement error. Participants are encouraged to bring a project idea or draft items and will leave with concrete feedback, revision priorities, piloting templates, and practical strategies for improving validity, reliability, cross-cultural comparability, and reproducibility. The seminar is delivered live via Zoom, with recordings and materials available for 30 days, an expert-moderated Q&A forum, and an official Instats certificate of completion provided at the end. #Education #MixedMethods #SocialTheory #Statistics #Research #ResearchTraining #Instats